I catalogued 200 real AI cases into tools and tech stacks. These are still evolving, but a landscape is starting to form. Here are the most popular ones: ML Platform (10.9%) — Building, training and deploying models: Amazon Bedrock, Google Vertex, IBM Watsonx, NVIDIA Isaac Lab (robotics) CRM & Sales (9.7%) — Managing pipelines and revenue ops: Salesforce, Apollo, Shopify Data Platform (7.3%) — Storing and processing data at scale: Databricks, Snowflake, Airtable Agentic Management (7%) — Deploying and managing AI across enterprise systems: Salesforce Agentforce, C3 AI, Moveworks, LangChain LLMs (7%) — Foundational models: Anthropic Claude, OpenAI GPT, Google Gemini Developer Tools (7%) — Building and monitoring agentic systems: Claude Code, LangSmith, Elastic Observability Still early but worth noting: Business Intelligence, Security, Healthcare, and Chatbots all sit below 3%. Worth mentioning that chatbots are starting to be labelled as agents instead of chatbots. How deployments are structured Platform-first (47%) — AI embedded within enterprise software, model selection abstracted from the user (e.g. Microsoft Copilot, Harvey, Granola). API-first (31%) — Direct integration with foundation models, common in smaller stacks and engineering-led environments. Hybrid (22%) — Combining direct model access with orchestration and enterprise data platforms. A few takeaways LLMs get most of the headlines but represent just 7% of the tooling showing up in real deployments. They do plenty of the heavy lifting, but the work is distributed across a much wider stack. The report also breaks down by business functions and industries. Engineering and Operations lead adoption, and Tech and Finance are moving fast, but the data shows real gaps across sectors and functions. Blue ocean in most directions. Full report and the living case AI map State of Applied AI 2026 submitted by /u/santanah8
Originally posted by u/santanah8 on r/ArtificialInteligence
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